Purpose This study explored to what extent and how researchers in five Korean government research institutes that implement research data management practices share their research data and investigated the challenges they perceive regarding data sharing.
Methods The study collected survey data from 224 respondents by posting a link to a SurveyMonkey questionnaire on the homepage of each of the five research institutes from June 15 to 29, 2022. Descriptive statistical analyses were conducted.
Results Among 148 respondents with data sharing experience, the majority had shared some or most of their data. Restricted data sharing within a project was more common than sharing data with outside researchers on request or making data publicly available. Sharing data directly with researchers who asked was the most common method of data sharing, while sharing data via institutional repositories was the second most common method. The most frequently cited factors impeding data sharing included the time and effort required to organize data, concerns about copyright or ownership of data, lack of recognition and reward, and concerns about data containing sensitive information.
Conclusion Researchers need ongoing training and support on making decisions about access to data, which are nuanced rather than binary. Research institutes’ commitment to developing and maintaining institutional data repositories is also important to facilitate data sharing. To address barriers to data sharing, it is necessary to implement research data management services that help reduce effort and mitigate concerns about legal issues. Possible incentives for researchers who share data should also continue to be explored.
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Korean scholarly journal editors’ and publishers’ attitudes towards journal data sharing policies and data papers (2023): a survey-based descriptive study Hyun Jun Yi, Youngim Jung, Hyekyong Hwang, Sung-Nam Cho Science Editing.2023; 10(2): 141. CrossRef
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Purpose This study investigated the current status and factors associated with adopting data sharing policies in Asian journals. Data sharing policies vary by country and region, and few studies have examined the trends and factors related to these policies in journals across the Asian region.
Methods The 2020 Scimago Journal and Country Rank was used to download data about 1,143 Asian journals indexed in Web of Science. Excluding 40 journals inaccessible via the Internet or without English-language websites and author guidelines, 1,103 journals were analyzed through descriptive statistical analyses and the chi-square test.
Results Of the 1,103 journals, 325 (29.5%) had data sharing policies, showing a moderate level of policy adoption among Asian journals. The results of the chi-square test suggested that the impact factor and publisher type (whether a publisher was commercial) were significantly associated with the presence of data sharing policies in journals, but subject categories were not identified as a significant factor. Regarding the strength of data sharing policies, most journals provided policies that only encouraged data sharing.
Conclusion Policies only encouraging data sharing are unlikely to lead to actual data sharing; thus, considering varying levels of policy strength and effective ways to induce authors’ compliance with the policies is important. Further research needs to examine other factors affecting the presence or strength of data sharing policies.
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Methods Among 79 data journals indexed in Web of Science, we selected four data journals where data papers accounted for more than 20% of the publication volume and whose corresponding authors belonged to South Korean research institutes. A qualitative analysis was conducted of the subjective experiences of seven corresponding authors who agreed to participate in interviews. To analyze interview transcriptions, clusters were created by restructuring the theme nodes using Nvivo 12.
Results The most important element of data journals to researchers was their usefulness for obtaining credit for research performance. Since the data in repositories linked to data papers are screened using journals’ review processes, the validity, accuracy, reusability, and reliability of data are ensured. In addition, data journals provide a basis for data sharing using repositories and data-centered follow-up research using citations and offer detailed descriptions of data.
Conclusion Data journals play a leading role in data-centered research. Data papers are recognized as research achievements through citations in the same way as research papers published in conventional journals, but there was also a perception that it is difficult to attain a similar level of academic recognition with data papers as with research papers. However, researchers highly valued the usefulness of data journals, and data journals should thus be developed into new academic communication channels that enhance data sharing and reuse.
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Development and validation of the motivation to publish scale-scientific articles (EMP-AC) for Peruvian university students Oscar Mamani-Benito, Julio Torres-Miranda, Edison Effer Apaza-Tarqui, Madona Tito-Betancur, Wilter C. Morales-García, Josué Edison Turpo-Chaparro Frontiers in Education.2023;[Epub] CrossRef
Korean scholarly journal editors’ and publishers’ attitudes towards journal data sharing policies and data papers (2023): a survey-based descriptive study Hyun Jun Yi, Youngim Jung, Hyekyong Hwang, Sung-Nam Cho Science Editing.2023; 10(2): 141. CrossRef
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Methods We analyzed peer review systems and criteria for peer review in nine data journals indexed by Web of Science, as well as the positions of the editorial committee members of the journals. Each data journal’s website was initially surveyed, and the editors-in-chief were queried via email about any information not found on the websites. The peer review criteria of the journals were analyzed in terms of data quality, metadata quality, and general quality.
Results Seven of the nine data journals adopted single-blind and open review peer review methods. The remaining two implemented modified models, such as interactive and community review. In the peer review criteria, there was a shared emphasis on the appropriateness of data production methodology and detailed descriptions. The editorial committees of the journals tended to have subject editors or subject advisory boards, while a few journals included positions with the responsibility of evaluating the technical quality of data.
Conclusion Creating a community of subject experts and securing various editorial positions for peer review are necessary for data journals to achieve data quality assurance and to promote reuse. New practices will emerge in terms of data peer review models, criteria, and editorial positions, and further research needs to be conducted.
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Methods A content analysis of 15 data paper templates/guidelines from 24 data journals indexed by the Web of Science was performed. A coding scheme was developed based on previous studies, consisting of four categories: general data set properties, data production information, repository information, and reuse information.
Results Only a few types of contextual information were commonly requested by the journals. Except data format information and file names, general data set properties were specified less often than other categories of contextual information. Researchers were frequently asked to provide data production information, such as information on the data collection, data producer, and related project. Repository information focused on data identifiers, while information about repository reputation and curation practices was rarely requested. Reuse information mostly involved advice on the reuse of data and terms of use.
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